1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
10/3/2024 Diosi 21081 Andrés pipeta
11/3/2024 Diosi 66970 Andrés n&d 50990 + lavanda 3asy clean 10k 79990x2
17/3/2024 Comida 55951 Tami Supermercado
19/3/2024 VTR 21990 Andrés NA
24/3/2024 Comida 94384 Tami Supermercado
27/3/2024 Comida 27980 Tami Barras Wild Soul
27/3/2024 Electricidad 56338 Andrés NA
29/3/2024 Comida 69144 Tami Supermercado
1/4/2024 Comida 11990 Andrés vino
1/4/2024 Comida 21300 Andrés piwen
2/4/2024 Comida 34980 Tami bar providencia
3/4/2024 Comida 30969 Tami Supermercado
5/4/2024 Enceres 24990 Tami Shampoo
5/4/2024 Enceres 16600 Tami Acondicionador cabello
6/4/2024 Enceres 16600 Andrés Acondicionador cabello
7/4/2024 Comida 33828 Tami Supermercado
10/4/2024 Comida 20230 Tami Supermercado
12/4/2024 Comida 7000 Andrés NA
13/4/2024 Comida 22800 Andrés empanadas
14/4/2024 Comida 75094 Tami Supermercado
18/4/2024 Parafina 49376 Tami NA
19/4/2024 VTR 21990 Andrés NA
22/4/2024 Comida 13990 Tami Barritas Wild Soul
22/4/2024 Comida 67379 Tami NA
23/4/2024 Enceres 7880 Andrés maacarillas 50 unidades
23/4/2024 prestamo 242000 Tami NA
27/4/2024 Comida 41406 Tami Supermercado
27/4/2024 Préstamo Andrés 122000 Tami NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 8.4693e+08   2    7.7855  5e-04 ***
## lag_depvar    1.0427e+11   1 1917.0616 <2e-16 ***
## Residuals     3.8074e+10 700                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff        lwr      upr    p adj
## 1-0  7228.838   868.8153 13588.86 0.021186
## 2-0 29159.353 23400.9351 34917.77 0.000000
## 2-1 21930.514 18557.2874 25303.74 0.000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
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## 558  62179.43             2   58060.86
## 559  57333.86             2   62179.43
## 560  70797.00             2   57333.86
## 561  89901.71             2   70797.00
## 562  78558.14             2   89901.71
## 563  65466.00             2   78558.14
## 564  70525.00             2   65466.00
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## 566  69736.29             2   68377.86
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## 568  41757.00             2   60085.86
## 569  49780.29             2   41757.00
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## 571  57894.29             2   56540.29
## 572  60270.29             2   57894.29
## 573  61011.00             2   60270.29
## 574  57721.43             2   61011.00
## 575  71741.00             2   57721.43
## 576  59576.00             2   71741.00
## 577  52390.29             2   59576.00
## 578  61092.29             2   52390.29
## 579  62814.00             2   61092.29
## 580  54908.29             2   62814.00
## 581  62082.00             2   54908.29
## 582  57017.71             2   62082.00
## 583  53634.43             2   57017.71
## 584  69169.00             2   53634.43
## 585  52488.14             2   69169.00
## 586  60895.57             2   52488.14
## 587  59856.57             2   60895.57
## 588  52670.00             2   59856.57
## 589  51874.57             2   52670.00
## 590  52190.57             2   51874.57
## 591  41562.43             2   52190.57
## 592  44764.14             2   41562.43
## 593  38612.71             2   44764.14
## 594  43473.14             2   38612.71
## 595  53505.00             2   43473.14
## 596  45870.86             2   53505.00
## 597  52578.00             2   45870.86
## 598  55300.00             2   52578.00
## 599  61789.71             2   55300.00
## 600  57391.71             2   61789.71
## 601  62902.29             2   57391.71
## 602  53250.43             2   62902.29
## 603  55402.57             2   53250.43
## 604  56291.29             2   55402.57
## 605  58933.57             2   56291.29
## 606  59590.71             2   58933.57
## 607  59065.00             2   59590.71
## 608  52399.57             2   59065.00
## 609  60483.43             2   52399.57
## 610  58262.71             2   60483.43
## 611  54939.71             2   58262.71
## 612  51169.00             2   54939.71
## 613  43113.29             2   51169.00
## 614  56289.71             2   43113.29
## 615  60739.86             2   56289.71
## 616  50363.14             2   60739.86
## 617  62270.86             2   50363.14
## 618  67061.57             2   62270.86
## 619  59609.00             2   67061.57
## 620  85054.00             2   59609.00
## 621  68023.29             2   85054.00
## 622  59242.29             2   68023.29
## 623  61535.14             2   59242.29
## 624  56215.86             2   61535.14
## 625  45152.29             2   56215.86
## 626  57409.57             2   45152.29
## 627  35151.43             2   57409.57
## 628  34991.43             2   35151.43
## 629  45944.71             2   34991.43
## 630  57944.71             2   45944.71
## 631  55706.29             2   57944.71
## 632  88593.71             2   55706.29
## 633  77359.43             2   88593.71
## 634  79878.71             2   77359.43
## 635  81753.00             2   79878.71
## 636  75716.00             2   81753.00
## 637  67381.43             2   75716.00
## 638  63528.57             2   67381.43
## 639  49682.86             2   63528.57
## 640  47815.00             2   49682.86
## 641  46546.14             2   47815.00
## 642  44808.71             2   46546.14
## 643  42959.57             2   44808.71
## 644  46023.86             2   42959.57
## 645  51309.57             2   46023.86
## 646  68447.29             2   51309.57
## 647  84959.29             2   68447.29
## 648  81666.29             2   84959.29
## 649  82700.86             2   81666.29
## 650  89422.14             2   82700.86
## 651 104812.71             2   89422.14
## 652  98812.71             2  104812.71
## 653  64779.86             2   98812.71
## 654  61862.86             2   64779.86
## 655  58376.43             2   61862.86
## 656  59503.57             2   58376.43
## 657  55429.43             2   59503.57
## 658  44454.57             2   55429.43
## 659  47184.00             2   44454.57
## 660  52126.71             2   47184.00
## 661  51202.00             2   52126.71
## 662  64437.14             2   51202.00
## 663  64297.14             2   64437.14
## 664  64628.57             2   64297.14
## 665  51413.14             2   64628.57
## 666  52969.43             2   51413.14
## 667  54135.29             2   52969.43
## 668  48799.43             2   54135.29
## 669  41907.86             2   48799.43
## 670  45382.00             2   41907.86
## 671  42633.29             2   45382.00
## 672  46624.71             2   42633.29
## 673  44051.86             2   46624.71
## 674  35852.86             2   44051.86
## 675  29737.71             2   35852.86
## 676  29734.86             2   29737.71
## 677  32881.71             2   29734.86
## 678  38298.57             2   32881.71
## 679  40886.14             2   38298.57
## 680  38601.86             2   40886.14
## 681  38628.86             2   38601.86
## 682  39142.57             2   38628.86
## 683  32666.14             2   39142.57
## 684  39911.57             2   32666.14
## 685  39336.29             2   39911.57
## 686  39678.86             2   39336.29
## 687  41963.14             2   39678.86
## 688  54220.57             2   41963.14
## 689  63901.86             2   54220.57
## 690  73116.00             2   63901.86
## 691  60863.86             2   73116.00
## 692  56293.86             2   60863.86
## 693  52725.00             2   56293.86
## 694  55525.00             2   52725.00
## 695  44413.00             2   55525.00
## 696  37200.14             2   44413.00
## 697  30212.43             2   37200.14
## 698  26456.71             2   30212.43
## 699  24716.71             2   26456.71
## 700  31020.29             2   24716.71
## 701  32132.57             2   31020.29
## 702  32902.57             2   32132.57
## 703  39694.14             2   32902.57
## 704  72501.29             2   39694.14
## 705  94845.00             2   72501.29
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   548 51393.61 15469.789
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  55525.00
## [694]  44413.00  37200.14  30212.43  26456.71  24716.71  31020.29  32132.57
## [701]  32902.57  39694.14  72501.29  94845.00
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   1950.36912   4011.01173   -508.89276   2463.36725  -2911.93174    539.71840 
##            8            9           10           11           12           13 
##  -5625.01276  -1222.51264  -4004.45524   -492.77814  -5003.84001  -1718.43753 
##           14           15           16           17           18           19 
##  -1008.06118    278.32381  -3318.68841   -476.76595  -2214.72903   6510.92436 
##           20           21           22           23           24           25 
##  -1525.29751  -1216.99669   1459.75514  -1176.52769    235.46447   1704.79248 
##           26           27           28           29           30           31 
##  -7066.88475    899.46213   8168.12669    501.69981     70.38800  -2320.61855 
##           32           33           34           35           36           37 
##   1623.67154   4639.29091   1246.54770   2515.92864  -1724.10068   4717.68908 
##           38           39           40           41           42           43 
##   4368.49457  -2166.65141  -2912.87300  -1085.44185 -10732.63893   7168.73769 
##           44           45           46           47           48           49 
##   2539.78897   1382.77518   8136.00697    810.86169   6646.51346   6896.15643 
##           50           51           52           53           54           55 
##  -5642.40733  -4655.77312  -4994.64298  -7931.82668   6032.41309  -4087.68633 
##           56           57           58           59           60           61 
##  -4952.37731   3746.89099    839.39653    -63.29295    115.10075  -5017.92239 
##           62           63           64           65           66           67 
##  18048.59223   3790.25060  -3471.17094   6035.09100   7511.60422  14874.03969 
##           68           69           70           71           72           73 
##   2074.95608 -12858.11828  -1153.98363   4761.48483  -4740.87748  -4323.31668 
##           74           75           76           77           78           79 
## -10478.57854   2358.39320  -5464.22722    943.52172  -6957.90303    385.37652 
##           80           81           82           83           84           85 
##  -2488.50296  -2838.38106  -4089.69422   -721.76077   2147.85938   3645.24417 
##           86           87           88           89           90           91 
##    419.80113   -528.24618    153.12904   4266.79360  -1141.78104   1156.01298 
##           92           93           94           95           96           97 
##  -2045.67746  -1052.02584    158.87315    261.08623  -7492.19428   2295.80557 
##           98           99          100          101          102          103 
##  -8657.15346  -3090.35872  -4204.66224  -1928.47422  -1448.78925   3002.89425 
##          104          105          106          107          108          109 
##  -2459.00902   2464.49858  -1240.02079    885.91101   2525.37683  -3176.87521 
##          110          111          112          113          114          115 
##  -4779.77306   -955.57945   1801.97182  11628.12962  -1159.62788   2726.75782 
##          116          117          118          119          120          121 
##   4346.16730   3626.99259   -948.87108  -4596.81859  -3675.31979   2318.58917 
##          122          123          124          125          126          127 
##  -1705.34865   1344.23943   8878.21667    970.93198    249.39622  -2415.14066 
##          128          129          130          131          132          133 
##   2718.33221   7140.12771   1173.81245  -8345.69117   1782.64685   4186.21474 
##          134          135          136          137          138          139 
##  -3069.68917  -1374.54254   -830.84102  -3869.38233   1146.62568   -512.57484 
##          140          141          142          143          144          145 
##  -2933.85955   1666.28604  -1905.31551  -7872.34732   1908.98828  -3568.80954 
##          146          147          148          149          150          151 
##   1983.39310   -335.70769    952.10664   -408.55571   1305.37795   1162.37072 
##          152          153          154          155          156          157 
##   3350.05036  -4826.91950  -1201.48263  -3272.88433   5886.25564   9756.68548 
##          158          159          160          161          162          163 
##  -3581.03709  -4950.06794   3396.55025     55.95076   2576.53706  -5983.89592 
##          164          165          166          167          168          169 
##  -6882.55855   3957.82099  17266.82731   3715.31544   -282.60730  -2351.66312 
##          170          171          172          173          174          175 
##  -1052.31229   3620.71220   -162.80173  -8023.40144   2813.36569   4311.97327 
##          176          177          178          179          180          181 
##    660.91622   8785.78172  -9119.83340  -3468.65193 -10784.69274 -11401.63757 
##          182          183          184          185          186          187 
##    961.67004   9066.12538  -1524.69948   5826.28329   6529.70205  13205.26495 
##          188          189          190          191          192          193 
##   8614.17334  -3815.76454   2626.49885  10529.22824  -1395.64177  -2255.15727 
##          194          195          196          197          198          199 
## -10150.82154  -6372.63014   1147.67622  -5300.39394  -9918.69186   5162.15025 
##          200          201          202          203          204          205 
##  -3205.65699  -1871.23375   -966.47990   6338.87192   9811.59821    617.48314 
##          206          207          208          209          210          211 
##   2957.34344   3151.07665   5857.32595  12953.51656  -5449.16966 -11158.35232 
##          212          213          214          215          216          217 
##  -5677.08918 -10664.85118  -5264.40293   1301.39711 -13194.68694  16081.40785 
##          218          219          220          221          222          223 
##   7715.61255   1525.34717  26695.67363  12815.00379   7713.57174  14427.18697 
##          224          225          226          227          228          229 
##  -3421.15388  -1363.45995   4079.11987    656.72778   3002.00849   9251.84937 
##          230          231          232          233          234          235 
##   6140.02683  -1575.82074  -1565.70651   9629.06014 -11233.39312  -7176.12130 
##          236          237          238          239          240          241 
##  -8533.62500 -10194.56232   2880.02799   1208.58206  -8412.44800  -9186.04882 
##          242          243          244          245          246          247 
##   8820.25542  -7910.12911   2271.16818 -10467.50439  -4316.48269   1144.59782 
##          248          249          250          251          252          253 
##    770.87036 -12512.89144   3332.17347   1825.26370   4021.35815   2007.91717 
##          254          255          256          257          258          259 
##  -1254.82467  11037.51138  20904.04737   3429.62421  -4045.33475   4250.63800 
##          260          261          262          263          264          265 
##  -1537.47799   3846.51151  -4725.96248 -10844.87666  -4810.81114   -651.69389 
##          266          267          268          269          270          271 
##  -5315.01007   8604.37181  -4348.05848   4075.77067  -2171.39012   4342.79571 
##          272          273          274          275          276          277 
##    668.79885   7265.03468  -1379.89616  12027.02067  -4470.74104   1763.48188 
##          278          279          280          281          282          283 
##   -333.59277   7869.21324  -4973.55932  -2721.48964 -11291.11059  -2818.71002 
##          284          285          286          287          288          289 
##  18489.20552   7818.31724   2831.04235   -529.07001    974.01847   6454.42352 
##          290          291          292          293          294          295 
##   6983.21429 -18628.34101 -11209.95529  -8300.34405   9423.51455   2951.42965 
##          296          297          298          299          300          301 
##  -1261.24550  27311.07702  10244.81145   5143.35166   9764.63882   3151.83679 
##          302          303          304          305          306          307 
##   -759.86978   8111.86454 -24042.51728  -3557.62320   -235.40272  -7028.03824 
##          308          309          310          311          312          313 
##  -4095.82177   2782.20880  -9298.58484  -3413.40791  -8378.19065   1319.71131 
##          314          315          316          317          318          319 
##  -3351.28680   1842.40595  -4245.02960  27261.97300   -650.28010   3338.21160 
##          320          321          322          323          324          325 
##  10892.08400   5733.93069  32548.28485   5550.72004 -20516.66932   1953.33139 
##          326          327          328          329          330          331 
##   1258.95531  -6333.50772  -1687.28717 -33249.19300    621.31173  -2513.73340 
##          332          333          334          335          336          337 
##   -291.66147  -3335.10612   3916.94858   -541.56716  -7043.05773  -3253.53926 
##          338          339          340          341          342          343 
##  -2333.47433  -7817.01652   3668.64652  -1490.61369  -1849.28318  -1101.70133 
##          344          345          346          347          348          349 
##     78.64374    402.33196  -1679.56263  -9511.11751 -13347.61353   2076.62245 
##          350          351          352          353          354          355 
##  -4500.38488  -3845.91880  -6170.02824   1537.78102   1219.21110   2624.86611 
##          356          357          358          359          360          361 
##  -3852.17224   -622.68398    581.88780   6937.75101    271.71469    -40.65080 
##          362          363          364          365          366          367 
##   2580.09527  -2730.98132   -883.92199  -8755.32610  -4707.89620  -6318.59271 
##          368          369          370          371          372          373 
##  -5089.54931  -7410.80112   4821.53988    249.10439   7019.73588  -7661.33995 
##          374          375          376          377          378          379 
##  -2346.45107  -3475.44079  -2567.16275 -12560.47260   1713.44835 -10777.31756 
##          380          381          382          383          384          385 
##   5487.24067   9206.66557   3093.33221  -2401.01204   1582.53018   6738.04424 
##          386          387          388          389          390          391 
##  11459.62822  -5670.44764  -5305.07916   -157.81455   8557.71394   1875.92524 
##          392          393          394          395          396          397 
##  11283.11505  -9741.11491   2801.31544    751.80038    594.95497   -627.94745 
##          398          399          400          401          402          403 
##   -552.78451 -14489.18868   8403.81304  -1211.39668  -1408.60285   6939.35550 
##          404          405          406          407          408          409 
##  -7913.55069  -1344.23813  -2580.16566  -5877.76877  -2951.32846  -4014.08614 
##          410          411          412          413          414          415 
##  -8864.67604   5974.85686   1557.21655  -7429.03880  -7795.36748  14075.50333 
##          416          417          418          419          420          421 
##   3803.88792   4508.58356  -7989.36140  -4768.08056  -2656.19551   2755.86331 
##          422          423          424          425          426          427 
## -14040.28888  -2924.02201  -9229.27173   2834.43219   6852.50613   6526.07921 
##          428          429          430          431          432          433 
##  -3976.91067  -4141.25359  -4769.07329  -1863.30050  -5785.12435  -6730.66669 
##          434          435          436          437          438          439 
##  -6087.02631  -1553.69946   -991.61140  -5100.59324   2438.87213   4741.65583 
##          440          441          442          443          444          445 
##  -5098.90639  -2231.17844   1500.61575  -3885.68514   2767.17650  -6610.38015 
##          446          447          448          449          450          451 
## -12186.85695  -4669.66160   9478.64633  -2091.30770   4692.33305  -5879.16996 
##          452          453          454          455          456          457 
##  -1173.24163    336.90808   2994.59260 -12265.86413   3276.20669  -6747.81646 
##          458          459          460          461          462          463 
##   6432.87553   2994.50994   2521.43109  -3808.17071   2098.37463     19.53088 
##          464          465          466          467          468          469 
##   1820.68579   -478.19160   3387.50520  -2575.55892   5843.09511  -6856.71393 
##          470          471          472          473          474          475 
##  -2941.54609  -2202.91054  -4672.34538   2958.27912   7796.24987  -5947.54407 
##          476          477          478          479          480          481 
##   1501.02624  -6145.20609  -2859.26651   1983.25575 -12930.47435  -9856.58859 
##          482          483          484          485          486          487 
##  -1364.01120   -121.26430  -1076.25739  -1440.64242  -9673.62137  10945.34377 
##          488          489          490          491          492          493 
##   6206.79861   7454.71578  -5338.75635   5412.09148   9382.50301   6213.93067 
##          494          495          496          497          498          499 
## -13280.35676 -10506.41198  -3470.94226  -1152.26036   -565.43697  -7657.61389 
##          500          501          502          503          504          505 
##    528.40865   4230.61774   5504.10788    710.70549    137.07133  -7182.01645 
##          506          507          508          509          510          511 
##    566.04656  -5038.48640   1806.82718  -1294.58584  -8158.87978   -663.62639 
##          512          513          514          515          516          517 
##  -2723.90709   -644.34386   1287.06414  -9514.31642  -7854.10265  24152.26548 
##          518          519          520          521          522          523 
##   9923.58953   6053.63640  -5130.14081   2935.87228  17166.97467  11749.45644 
##          524          525          526          527          528          529 
## -23810.86859  -4968.06148  -3690.84698   4583.70501   -301.97846 -11052.61461 
##          530          531          532          533          534          535 
##   4347.37492  13915.74429  -4848.21909   4447.35321   5660.20177  -1648.48829 
##          536          537          538          539          540          541 
##  -4429.13929  -7010.50837  -2098.76177   8313.69142    196.80489  -8074.09018 
##          542          543          544          545          546          547 
##   1812.12790   -580.24537    385.05944 -11004.36656 -11124.48630   1901.20001 
##          548          549          550          551          552          553 
##   6909.06460  -1331.58838    821.59134  -7718.94088   8509.10949    939.05757 
##          554          555          556          557          558          559 
## -11901.39488   9105.23037   8692.81268    208.02227   4953.17121  -3442.06213 
##          560          561          562          563          564          565 
##  14197.32014  21698.59227  -6110.70706  -9426.19701   6916.49239    409.16302 
##          566          567          568          569          570          571 
##   3618.14374  -7203.06995 -17214.53858   6605.78916   6450.78182   1978.55909 
##          572          573          574          575          576          577 
##   3187.59081   1880.50847  -2047.46036  14807.28501  -9440.72472  -6141.82369 
##          578          579          580          581          582          583 
##   8753.30825   2975.05357  -6414.54990   7572.84076  -3674.23449  -2692.77811 
##          584          585          586          587          588          589 
##  15757.73653 -14311.85925   8472.25410    187.16661  -6103.92473   -705.48262 
##          590          591          592          593          594          595 
##    296.07123 -10604.42168   1757.34105  -7153.54027   3008.60324   8851.41609 
##          596          597          598          599          600          601 
##  -7428.85607   5857.90463   2799.23774   6942.94932  -3048.32300   6252.74067 
##          602          603          604          605          606          607 
##  -8148.49761   2322.26499   1356.11779   3232.44976   1612.29304    520.20884 
##          608          609          610          611          612          613 
##  -5692.12380   8136.44806  -1051.47810  -2460.51714  -3367.24650  -8173.10556 
##          614          615          616          617          618          619 
##  11946.27947   5040.08983  -9172.05692  11679.00776   6206.85286  -5374.67840 
##          620          621          622          623          624          625 
##  26493.44898 -12467.47794  -6570.26275   3290.65088  -4004.77336 -10483.82654 
##          626          627          628          629          630          631 
##  11308.78925 -21513.50696  -2489.94218   8601.24230  11160.96386  -1419.87192 
##          632          633          634          635          636          637 
##  33396.78496  -6182.09896   6019.64989   5722.64570  -1929.73980  -5061.21853 
##          638          639          640          641          642          643 
##  -1730.78137 -12255.84420  -2190.53339  -1849.54560  -2493.38774  -2845.09778 
##          644          645          646          647          648          649 
##   1812.90364   4457.61037  17039.74052  18781.30553   1257.15320   5129.85354 
##          650          651          652          653          654          655 
##  10959.47601  20557.19128   1292.56153 -27569.09200  -1154.28640  -2126.64815 
##          656          657          658          659          660          661 
##   2005.33338  -3040.25701 -10503.74378   1684.55493   4274.86407   -909.81387 
##          662          663          664          665          666          667 
##  13122.30996   1575.37354   2027.46353 -11473.61248   1472.61856   1297.16398 
##          668          669          670          671          672          673 
##  -5043.50727  -7336.27808   2077.48461  -3665.47968   2694.97576  -3317.96302 
##          674          675          676          677          678          679 
##  -9299.50167  -8348.19479  -3080.61047     68.70915   2773.39313    692.35269 
##          680          681          682          683          684          685 
##  -3822.07614  -1826.32506  -1335.88119  -8255.06329   4572.18709  -2247.69636 
##          686          687          688          689          690          691 
##  -1409.30500    579.72961  10868.40709   9985.41633  10855.57592  -9337.93507 
##          692          693          694          695          696          697 
##  -3348.21418  -2978.33790   2897.54325 -10627.68510  -8263.47316  -9034.66196 
##          698          699          700          701          702          703 
##  -6767.89403  -5270.96682   2532.25366  -1788.30252  -1976.94517   4150.98847 
##          704          705 
##  31104.69821  25173.00991 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17318.92 20127.99 24325.04 24046.78 26368.65 23737.00 24443.73 19739.66 
##       10       11       12       13       14       15       16       17 
## 19479.74 16858.06 17625.13 14398.29 14448.78 15104.53 16778.40 15120.91 
##       18       19       20       21       22       23       24       25 
## 16141.73 15523.65 22511.30 21607.57 21094.39 22959.10 22294.11 22937.92 
##       26       27       28       29       30       31       32       33 
## 24759.17 18768.82 20471.87 28204.30 28261.18 27938.48 25599.61 26983.28 
##       34       35       36       37       38       39       40       41 
## 30774.88 31118.64 32508.96 30052.88 34074.51 37239.65 34335.16 31188.73 
##       42       43       44       45       46       47       48       49 
## 30051.92 20757.55 28175.64 30579.51 31654.14 38400.71 37902.06 42501.84 
##       50       51       52       53       54       55       56       57 
## 46681.41 39477.06 34118.21 29207.54 22443.73 28649.54 25275.95 21623.11 
##       58       59       60       61       62       63       64       65 
## 25972.46 27215.15 27508.18 27914.49 23840.69 40209.89 42029.17 37338.77 
##       66       67       68       69       70       71       72       73 
## 41489.40 46339.25 56864.62 54904.98 40345.70 37884.94 40862.45 35238.89 
##       74       75       76       77       78       79       80       81 
## 30752.01 21579.89 24738.51 20718.76 22776.90 17740.77 19729.22 18966.10 
##       82       83       84       85       86       87       88       89 
## 18006.84 16101.62 17362.28 20922.04 25280.63 26257.25 26281.87 26890.35 
##       90       91       92       93       94       95       96       97 
## 30960.21 29806.42 30792.39 28882.74 28093.27 28456.49 28857.62 22521.05 
##       98       99      100      101      102      103      104      105 
## 25495.72 18619.50 17490.95 15557.90 15853.65 16521.96 20934.72 20030.50 
##      106      107      108      109      110      111      112      113 
## 23494.59 23287.37 24941.05 27779.30 25310.92 21802.01 22073.74 24684.58 
##      114      115      116      117      118      119      120      121 
## 35403.63 33620.67 35433.55 38391.72 40321.44 38040.82 32931.18 29321.55 
##      122      123      124      125      126      127      128      129 
## 31376.49 29679.47 30845.21 38343.21 37990.46 37064.57 33970.10 35727.44 
##      130      131      132      133      134      135      136      137 
## 41053.04 40500.83 31820.35 33068.21 36215.26 32673.97 31082.84 30180.10 
##      138      139      140      141      142      143      144      145 
## 26783.23 28178.72 27951.43 25668.71 27666.03 26309.20 19997.01 22986.95 
##      146      147      148      149      150      151      152      153 
## 20842.75 23779.99 24312.75 25881.84 26061.48 27693.49 28976.81 31968.35 
##      154      155      156      157      158      159      160      161 
## 27499.20 26772.03 24360.03 30175.17 41601.47 39954.07 37354.31 42307.33 
##      162      163      164      165      166      167      168      169 
## 43697.03 47067.18 42593.84 37963.89 43316.46 59400.26 61582.75 60018.09 
##      170      171      172      173      174      175      176      177 
## 56886.31 55307.00 57973.37 57010.54 49405.92 52191.60 55884.08 55919.79 
##      178      179      180      181      182      183      184      185 
## 62953.12 53582.65 50377.12 41308.92 32961.62 36422.87 46390.99 45854.29 
##      186      187      188      189      190      191      192      193 
## 51727.30 57395.31 68033.83 73245.91 67025.07 67215.91 74191.50 69925.87 
##      194      195      196      197      198      199      200      201 
## 65508.68 54896.63 49006.75 50411.97 46065.69 38339.42 44678.09 42929.23 
##      202      203      204      205      206      207      208      209 
## 42572.05 43043.99 49746.97 58517.09 58151.66 59853.35 61486.96 65227.34 
##      210      211      212      213      214      215      216      217 
## 74567.03 66755.92 55103.23 49784.28 40901.26 37899.75 40971.69 31125.59 
##      218      219      220      221      222      223      224      225 
## 47871.67 55094.37 55984.18 78444.57 85839.14 87815.53 95305.15 86377.32 
##      226      227      228      229      230      231      232      233 
## 80456.17 80043.70 76738.56 75911.29 80584.83 81930.82 76440.85 71717.94 
##      234      235      236      237      238      239      240      241 
## 77295.82 64122.55 56265.77 48324.28 40048.26 44183.99 46307.88 39846.33 
##      242      243      244      245      246      247      248      249 
## 33610.60 43755.27 38079.26 41962.22 34329.77 33052.97 36659.27 39445.32 
##      250      251      252      253      254      255      256      257 
## 30397.68 36256.16 40006.64 45131.80 47813.68 47313.06 57475.95 74738.66 
##      258      259      260      261      262      263      264      265 
## 74556.19 67956.50 69418.48 65689.92 67116.68 60958.02 50376.38 46456.98 
##      266      267      268      269      270      271      272      273 
## 46663.58 42822.49 51508.63 47831.66 51922.82 50064.63 54077.49 54369.54 
##      274      275      276      277      278      279      280      281 
## 60306.32 57972.27 67515.60 61521.80 61729.02 60100.22 65766.13 59580.63 
##      282      283      284      285      286      287      288      289 
## 56190.54 45882.85 44301.08 61302.40 66758.39 67162.36 64614.55 63714.15 
##      290      291      292      293      294      295      296      297 
## 67661.50 71519.34 52770.53 43005.20 37096.49 47279.57 50477.96 49603.78 
##      298      299      300      301      302      303      304      305 
## 73475.90 79341.65 80000.36 84551.02 82773.73 77870.56 81290.95 56526.05 
##      306      307      308      309      310      311      312      313 
## 52837.26 52521.32 46394.68 43641.51 47196.58 39848.55 38587.76 33222.15 
##      314      315      316      317      318      319      320      321 
## 36956.00 36148.31 39928.46 37939.88 63380.85 61250.93 62852.77 70743.78 
##      322      323      324      325      326      327      328      329 
## 73099.14 98239.57 96638.96 72792.81 71606.76 69986.08 62045.57 59206.34 
##      330      331      332      333      334      335      336      337 
## 29557.12 33195.30 33628.95 35917.82 35267.48 40957.28 42018.49 37329.68 
##      338      339      340      341      342      343      344      345 
## 36554.62 36679.59 32061.21 37979.90 38634.43 38889.42 39753.50 41515.53 
##      346      347      348      349      350      351      352      353 
## 43313.13 43068.12 36107.18 26801.23 32074.38 30950.63 30546.17 28194.50 
##      354      355      356      357      358      359      360      361 
## 32810.79 36514.85 40918.74 39131.97 40375.40 42485.25 49781.57 50324.79 
##      362      363      364      365      366      367      368      369 
## 50523.76 52953.98 50471.06 49923.04 42666.61 39900.88 36128.98 33937.37 
##      370      371      372      373      374      375      376      377 
## 30047.89 37238.32 39494.69 47274.77 41327.02 40781.58 39338.45 38877.47 
##      378      379      380      381      382      383      384      385 
## 29867.27 34403.89 27548.47 35657.91 45852.81 49370.58 47667.04 49632.10 
##      386      387      388      389      390      391      392      393 
## 55769.09 65127.73 58429.79 52971.96 52704.29 59985.22 60501.60 69054.40 
##      394      395      396      397      398      399      400      401 
## 58305.68 59851.63 59417.62 58908.38 57415.50 56193.62 43129.19 51600.11 
##      402      403      404      405      406      407      408      409 
## 50613.89 49593.93 55909.69 48551.81 47872.17 46221.20 41956.19 40802.51 
##      410      411      412      413      414      415      416      417 
## 38892.25 33065.29 40832.93 43720.18 38463.65 33617.50 48290.54 52083.99 
##      418      419      420      421      422      423      424      425 
## 55960.79 48530.51 44902.91 43596.57 47135.15 35708.88 35441.70 29777.14 
##      426      427      428      429      430      431      432      433 
## 35292.35 43508.78 50308.91 47117.54 44225.36 41191.59 41081.27 37606.10 
##      434      435      436      437      438      439      440      441 
## 33796.03 31066.99 32622.04 34446.74 32477.99 37279.20 43401.91 40197.61 
##      442      443      444      445      446      447      448      449 
## 39907.53 42873.83 40788.11 44724.38 40034.71 31186.66 30039.64 41245.02 
##      450      451      452      453      454      455      456      457 
## 40930.81 46506.60 42200.96 42545.95 44144.84 47813.44 37822.79 42607.39 
##      458      459      460      461      462      463      464      465 
## 38091.70 45559.78 49032.85 51618.46 48391.63 50701.18 50900.03 52623.76 
##      466      467      468      469      470      471      472      473 
## 52128.07 55032.56 52396.48 57380.29 50730.12 48372.91 46977.92 43647.29 
##      474      475      476      477      478      479      480      481 
## 47353.32 54717.12 49218.40 50898.92 45757.27 44157.89 46953.05 36508.45 
##      482      483      484      485      486      487      488      489 
## 30155.87 32000.26 34660.97 36131.07 37084.05 30809.66 43172.77 49744.14 
##      490      491      492      493      494      495      496      497 
## 56483.33 51265.34 56033.93 63565.78 67326.36 53765.98 44469.51 42520.83 
##      498      499      500      501      502      503      504      505 
## 42839.72 43620.33 38180.59 40547.53 45778.32 51384.15 52084.36 52193.45 
##      506      507      508      509      510      511      512      513 
## 45979.38 47301.49 43610.60 46329.30 45999.45 39799.05 40915.05 40101.20 
##      514      515      516      517      518      519      520      521 
## 41192.08 43796.89 36732.53 32074.88 55645.84 63697.65 67301.86 60769.27 
##      522      523      524      525      526      527      528      529 
## 62090.88 75495.26 82378.87 57663.35 52601.85 49340.29 53660.84 53173.76 
##      530      531      532      533      534      535      536      537 
## 43488.34 48413.54 60905.08 55499.08 58851.37 62785.92 59877.85 54974.94 
##      538      539      540      541      542      543      544      545 
## 48524.48 47198.31 55029.48 54783.23 47442.59 49636.53 49465.51 50150.08 
##      546      547      548      549      550      551      552      553 
## 40923.91 32868.66 37152.51 45160.73 44960.41 46643.51 40733.32 49625.94 
##      554      555      556      557      558      559      560      561 
## 50765.82 40681.48 50095.04 57852.83 57226.26 60775.92 56599.68 68203.12 
##      562      563      564      565      566      567      568      569 
## 84668.85 74892.20 63608.51 67968.69 66118.14 67288.93 58971.54 43174.50 
##      570      571      572      573      574      575      576      577 
## 50089.50 55915.73 57082.69 59130.49 59768.89 56933.71 69016.72 58532.11 
##      578      579      580      581      582      583      584      585 
## 52338.98 59838.95 61322.84 54509.16 60691.95 56327.21 53411.26 66800.00 
##      586      587      588      589      590      591      592      593 
## 52423.32 59669.40 58773.92 52580.05 51894.50 52166.85 43006.80 45766.25 
##      594      595      596      597      598      599      600      601 
## 40464.54 44653.58 53299.71 46720.10 52500.76 54846.76 60440.04 56649.55 
##      602      603      604      605      606      607      608      609 
## 61398.93 53080.31 54935.17 55701.12 57978.42 58544.79 58091.70 52346.98 
##      610      611      612      613      614      615      616      617 
## 59314.19 57400.23 54536.25 51286.39 44343.43 55699.77 59535.20 50591.85 
##      618      619      620      621      622      623      624      625 
## 60854.72 64983.68 58560.55 80490.76 65812.55 58244.49 60220.63 55636.11 
##      626      627      628      629      630      631      632      633 
## 46100.78 56664.94 37481.37 37343.47 46783.75 57126.16 55196.93 83541.53 
##      634      635      636      637      638      639      640      641 
## 73859.06 76030.35 77645.74 72442.65 65259.35 61938.70 50005.53 48395.69 
##      642      643      644      645      646      647      648      649 
## 47302.10 45804.67 44210.95 46851.96 51407.55 66177.98 80409.13 77571.00 
##      650      651      652      653      654      655      656      657 
## 78462.67 84255.52 97520.15 92348.95 63017.14 60503.08 57498.24 58469.69 
##      658      659      660      661      662      663      664      665 
## 54958.32 45499.45 47851.85 52111.81 51314.83 62721.77 62601.11 62886.76 
##      666      667      668      669      670      671      672      673 
## 51496.81 52838.12 53842.94 49244.14 43304.52 46298.77 43929.74 47369.82 
##      674      675      676      677      678      679      680      681 
## 45152.36 38085.91 32815.47 32813.01 35525.18 40193.79 42423.93 40455.18 
##      682      683      684      685      686      687      688      689 
## 40478.45 40921.21 35339.38 41583.98 41088.16 41383.41 43352.16 53916.44 
##      690      691      692      693      694      695      696      697 
## 62260.42 70201.79 59642.07 55703.34 52627.46 55040.69 45463.62 39247.09 
##      698      699      700      701      702      703      704      705 
## 33224.61 29987.68 28488.03 33920.87 34879.52 35543.15 41396.59 69671.99 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8191
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    7.785548  0.5053083    3.421556
## t2* 1917.061615 22.6012688  227.866197
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    3.410057       7.941646   14.47201
## 2    lag_depvar 1588.923748    1925.921196 2335.12468

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Apr 29 00:39:43 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Apr 29 00:39:50 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Apr 29 00:39:57 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Apr 29 00:40:04 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Apr 29 00:40:11 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Apr 29 00:40:19 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Apr 29 00:40:26 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Apr 29 00:40:33 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Apr 29 00:40:40 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Apr 29 00:40:47 2024
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_24 %>% 
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))
Item 2024 2023 2022 2021 2020
Agua 5.516333 5.195333 5.410333 5.849167 6.4088462
Comida 288.100000 366.009167 310.278417 317.896583 344.0515385
Comunicaciones 0.000000 0.000000 0.000000 0.000000 0.0000000
Electricidad 74.633333 38.104750 47.072333 29.523000 35.5299231
Enceres 38.339333 18.259750 20.086417 14.801167 24.7500000
Farmacia 0.000000 4.733250 1.831667 13.996083 7.9840962
Gas/Bencina 23.665667 35.219333 44.325000 13.583667 27.7886346
Diosi 29.350333 55.804250 31.180667 52.687833 42.4919231
donaciones/regalos 0.000000 0.000000 0.000000 14.340167 5.2830577
Electrodomésticos/ Mantención casa 20.000000 0.000000 3.944000 56.595000 17.1051538
VTR 21.990000 12.829167 25.156667 19.086917 19.2730385
Netflix 5.565667 4.555500 7.151583 7.028750 6.5479615
Otros 0.000000 0.000000 3.151083 0.000000 0.7271731
Total 507.160667 540.710500 499.588167 545.388333 537.9413462
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")

tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  47 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2321, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2024-05-09 00:04:58 sería de: 37.727 pesos// Percentil 95% más alto proyectado: 40.830,8

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 37314.39 37312.97
Lo.80 37326.45 37324.65
Point.Forecast 37726.90 38784.71
Hi.80 39482.91 43631.32
Hi.95 40444.95 46196.96


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.2428  1018.1513
## s.e.  0.1280    27.5653
## 
## sigma^2 = 28060:  log likelihood = -404.49
## AIC=814.99   AICc=815.4   BIC=821.37
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.2061   694.2043  10.2795
## s.e.  0.1300   248.5873   7.8333
## 
## sigma^2 = 27809:  log likelihood = -403.68
## AIC=815.37   AICc=816.07   BIC=823.88
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 775.9811 679.7095 726.3631
Lo.80 891.5957 796.8561 810.9225
Point.Forecast 1109.9967 1018.1513 998.4389
Hi.80 1328.3978 1239.4465 1278.9932
Hi.95 1444.0124 1356.5931 1458.1362


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [4] Boom_0.9.15         scales_1.3.0        ggiraph_0.8.9      
##  [7] tidytext_0.4.1      DT_0.32             janitor_2.2.0      
## [10] autoplotly_0.1.4    rvest_1.0.4         plotly_4.10.4      
## [13] xts_0.13.2          forecast_8.21.1     wordcloud_2.6      
## [16] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-11          
## [19] NLP_0.2-1           tsibble_1.1.4       lubridate_1.9.3    
## [22] forcats_1.0.0       dplyr_1.1.4         purrr_1.0.2        
## [25] tidyr_1.3.1         tibble_3.2.1        tidyverse_2.0.0    
## [28] gsynth_1.2.1        lattice_0.20-45     GGally_2.2.1       
## [31] ggplot2_3.5.0       gridExtra_2.3       plotrix_3.8-4      
## [34] sparklyr_1.8.4      httr_1.4.7          readxl_1.4.3       
## [37] zoo_1.8-12          stringr_1.5.1       stringi_1.8.3      
## [40] data.table_1.15.0   reshape2_1.4.4      fUnitRoots_4021.80 
## [43] plyr_1.8.9          readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.2-0          systemfonts_1.0.5   selectr_0.4-2      
##   [4] lazyeval_0.2.2      websocket_1.4.1     crosstalk_1.2.1    
##   [7] listenv_0.9.1       digest_0.6.34       foreach_1.5.2      
##  [10] htmltools_0.5.7     fansi_1.0.6         ggfortify_0.4.16   
##  [13] magrittr_2.0.3      doParallel_1.0.17   tzdb_0.4.0         
##  [16] globals_0.16.2      vroom_1.6.5         sandwich_3.1-0     
##  [19] askpass_1.2.0       timechange_0.3.0    anytime_0.3.9      
##  [22] tseries_0.10-55     colorspace_2.1-0    xfun_0.42          
##  [25] crayon_1.5.2        jsonlite_1.8.8      iterators_1.0.14   
##  [28] glue_1.7.0          gtable_0.3.4        car_3.1-2          
##  [31] quantmod_0.4.26     abind_1.4-5         mvtnorm_1.2-4      
##  [34] DBI_1.2.2           rngtools_1.5.2      Rcpp_1.0.12        
##  [37] lfe_2.9-0           viridisLite_0.4.2   xtable_1.8-4       
##  [40] bit_4.0.5           Formula_1.2-5       htmlwidgets_1.6.4  
##  [43] timeSeries_4032.109 gplots_3.1.3.1      ellipsis_0.3.2     
##  [46] spatial_7.3-14      farver_2.1.1        pkgconfig_2.0.3    
##  [49] nnet_7.3-16         sass_0.4.8          dbplyr_2.4.0       
##  [52] chromote_0.2.0      utf8_1.2.4          labeling_0.4.3     
##  [55] tidyselect_1.2.0    rlang_1.1.3         later_1.3.2        
##  [58] munsell_0.5.0       cellranger_1.1.0    tools_4.1.2        
##  [61] cachem_1.0.8        cli_3.6.2           generics_0.1.3     
##  [64] evaluate_0.23       fastmap_1.1.1       yaml_2.3.8         
##  [67] processx_3.8.3      knitr_1.45          bit64_4.0.5        
##  [70] caTools_1.18.2      future_1.33.1       nlme_3.1-153       
##  [73] doRNG_1.8.6         slam_0.1-50         xml2_1.3.6         
##  [76] tokenizers_0.3.0    compiler_4.1.2      rstudioapi_0.15.0  
##  [79] curl_5.2.0          bslib_0.6.1         highr_0.10         
##  [82] ps_1.7.6            fBasics_4032.96     Matrix_1.6-5       
##  [85] its.analysis_1.6.0  urca_1.3-3          vctrs_0.6.5        
##  [88] pillar_1.9.0        lifecycle_1.0.4     lmtest_0.9-40      
##  [91] jquerylib_0.1.4     bitops_1.0-7        R6_2.5.1           
##  [94] promises_1.2.1      KernSmooth_2.23-20  janeaustenr_1.0.0  
##  [97] parallelly_1.37.0   codetools_0.2-18    ggstats_0.5.1      
## [100] assertthat_0.2.1    boot_1.3-28         gtools_3.9.5       
## [103] MASS_7.3-54         openssl_2.1.1       withr_3.0.0        
## [106] fracdiff_1.5-3      parallel_4.1.2      hms_1.1.3          
## [109] quadprog_1.5-8      timeDate_4032.109   rmarkdown_2.25     
## [112] snakecase_0.11.1    carData_3.0-5       TTR_0.24.4
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))